Volume 8, Issue 2
Review of Methods Inspired by Algebraic-Multigrid for Data and Image Analysis Applications

Meirav Galun, Ronen Basri & Irad Yavneh

Numer. Math. Theor. Meth. Appl.,8 (2015), pp. 283-312

Published online: 2015-08

Preview Purchase PDF 1 977
Export citation
  • Abstract

Algebraic Multigrid (AMG) methods were developed originally for numerically solving Partial Differential Equations (PDE), not necessarily on structured grids. In the last two decades solvers inspired by the AMG approach, were developed for non PDE problems, including data and image analysis problems, such as clustering, segmentation, quantization and others. These solvers share a common principle in that there is a crosstalk between fine and coarse representations of the problems, with flow of information in both directions, fine-to-coarse and coarse-to-fine. This paper surveys some of these problems and the AMG-inspired algorithms for their solution.

  • Keywords

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{NMTMA-8-283, author = {Meirav Galun, Ronen Basri and Irad Yavneh}, title = {Review of Methods Inspired by Algebraic-Multigrid for Data and Image Analysis Applications}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2015}, volume = {8}, number = {2}, pages = {283--312}, abstract = {

Algebraic Multigrid (AMG) methods were developed originally for numerically solving Partial Differential Equations (PDE), not necessarily on structured grids. In the last two decades solvers inspired by the AMG approach, were developed for non PDE problems, including data and image analysis problems, such as clustering, segmentation, quantization and others. These solvers share a common principle in that there is a crosstalk between fine and coarse representations of the problems, with flow of information in both directions, fine-to-coarse and coarse-to-fine. This paper surveys some of these problems and the AMG-inspired algorithms for their solution.}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2015.w14si}, url = {http://global-sci.org/intro/article_detail/nmtma/12411.html} }

TY - JOUR T1 - Review of Methods Inspired by Algebraic-Multigrid for Data and Image Analysis Applications AU - Meirav Galun, Ronen Basri & Irad Yavneh JO - Numerical Mathematics: Theory, Methods and Applications VL - 2 SP - 283 EP - 312 PY - 2015 DA - 2015/08 SN - 8 DO - http://dor.org/10.4208/nmtma.2015.w14si UR - https://global-sci.org/intro/article_detail/nmtma/12411.html KW - AB -

Algebraic Multigrid (AMG) methods were developed originally for numerically solving Partial Differential Equations (PDE), not necessarily on structured grids. In the last two decades solvers inspired by the AMG approach, were developed for non PDE problems, including data and image analysis problems, such as clustering, segmentation, quantization and others. These solvers share a common principle in that there is a crosstalk between fine and coarse representations of the problems, with flow of information in both directions, fine-to-coarse and coarse-to-fine. This paper surveys some of these problems and the AMG-inspired algorithms for their solution.

Meirav Galun, Ronen Basri & Irad Yavneh. (1970). Review of Methods Inspired by Algebraic-Multigrid for Data and Image Analysis Applications. Numerical Mathematics: Theory, Methods and Applications. 8 (2). 283-312. doi:10.4208/nmtma.2015.w14si
Copy to clipboard
The citation has been copied to your clipboard